manta-lm-small / modeling_manta.py
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# coding=utf-8
# Copyright 2022 Mesh TensorFlow authors, Manta Authors and HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch Manta model."""
import math
from dataclasses import dataclass
import warnings
from typing import Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from transformers.modeling_outputs import BaseModelOutput, Seq2SeqLMOutput, Seq2SeqModelOutput
from transformers.modeling_utils import PreTrainedModel
from transformers.models.longformer import LongformerConfig, LongformerModel
from transformers.models.t5.configuration_t5 import T5Config
from transformers.models.t5.modeling_t5 import (
__HEAD_MASK_WARNING_MSG,
T5Attention,
T5Stack,
)
from transformers.utils import (
DUMMY_INPUTS,
DUMMY_MASK,
add_start_docstrings,
add_end_docstrings,
is_torch_fx_proxy,
logging,
replace_return_docstrings,
)
from .configuration_manta import MantaConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "MantaConfig"
_TOKENIZER_FOR_DOC = "ByT5Tokenizer"
MANTA_PRETRAINED_MODEL_ARCHIVE_LIST = []
def gaussian_pdf(x):
return torch.exp(-x * x / 2.0)
def pad_block_embeddings(block_embeddings, pad_length):
if not pad_length:
return block_embeddings
padding_tensor_len = max(pad_length - block_embeddings.size(1), 0)
padding_tensor = torch.zeros(
(block_embeddings.size(0), padding_tensor_len, block_embeddings.size(2)),
device=block_embeddings.device,
dtype=block_embeddings.dtype,
)
return torch.cat([block_embeddings[:, :pad_length, :], padding_tensor], dim=1)
@add_end_docstrings()
@dataclass
class MantaSeq2SeqLMOutput(Seq2SeqLMOutput):
"""
Base class for Manta encoder's outputs that also contains : pre-computed hidden states that can speed up sequential
decoding.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the decoder of the model.
If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
hidden_size)` is output.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the decoder at the output of each layer plus the optional initial embedding outputs.
decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the encoder at the output of each layer plus the optional initial embedding outputs.
encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
frontier_predictions: (`torch.FloatTensor`, *optional*, of shape `(batch_size, sequence_length, 1)`):
Probability scores of being a frontier as predicted by the FrontierPredictor module.
"""
frontier_predictions: Optional[torch.FloatTensor] = None
@dataclass
class MantaBaseModelOutput(BaseModelOutput):
"""
Base class for Manta's outputs, with potential hidden states, attentions and Manta's frontier predictions.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
frontier_predictions: (`torch.FloatTensor`, *optional*, of shape `(batch_size, sequence_length, 1)`):
Probability scores of being a frontier as predicted by the FrontierPredictor module.
"""
frontier_predictions: Optional[torch.FloatTensor] = None
class MantaFrontierPredictor(nn.Module):
def __init__(
self,
hidden_size,
num_layers,
num_attention_heads,
dropout_rate,
attention_window,
max_length,
):
super().__init__()
# First, find out what the maximum position will be after tensors are padded to a multiple of local_transformer_attention_window.
# Then, add 1 because LongFormer position embeddings are bugged when passed inputs_embeds.
max_position_embeddings = (max_length // attention_window + 1) * attention_window + 1
self.hidden_size = hidden_size
self.config = LongformerConfig(
attention_probs_dropout_prob=dropout_rate,
attention_window=attention_window,
hidden_act="gelu",
hidden_dropout_prob=dropout_rate,
hidden_size=hidden_size,
intermediate_size=hidden_size * 4,
max_position_embeddings=max_position_embeddings,
num_attention_heads=num_attention_heads,
num_hidden_layers=num_layers,
position_embedding_type="absolute", # Actually cannot be changed
vocab_size=1, # Remove almost entirely the embeddings
pad_token_id=0,
)
self.local_transformer = LongformerModel(self.config)
self.output_projection = nn.Linear(hidden_size, 1)
def forward(self, embeddings, attention_mask):
longformer_output = self.local_transformer(inputs_embeds=embeddings, attention_mask=attention_mask)
projection_outputs = self.output_projection(longformer_output.last_hidden_state)
frontier_predictions = torch.sigmoid(projection_outputs.squeeze(-1))
return frontier_predictions
class MantaConvFeatures(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size,
groups,
padding,
):
"""
This nn.Module "decomposes" the convolution in order to extract and cache feature maps. This amounts to
computing an element-wise multiplication between weights of size (hidden_dim, kernel_size) and the input.
"""
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.groups = groups
self.padding = padding
if groups == in_channels:
assert (
in_channels == out_channels
), "When using `groups = in_channels`, make sure to have `in_channels == out_channels`"
self.weight = nn.Parameter(torch.Tensor(1, 1, kernel_size, out_channels))
elif self.groups == 1:
self.weight = nn.Parameter(torch.Tensor(in_channels, out_channels, kernel_size))
else:
raise ValueError("MantaConvFeatures only supports `groups = 1` or `groups = in_channels`")
left_pad = (kernel_size - 1) // 2
self.pad = (left_pad, kernel_size - 1 - left_pad)
self.reset_parameters()
def reset_parameters(self):
"""
See https://pytorch.org/docs/stable/_modules/torch/nn/modules/conv.html#Conv1d, in the `_ConvNd` class :
> Setting a=sqrt(5) in kaiming_uniform is the same as initializing with
> uniform(-1/sqrt(k), 1/sqrt(k)), where k = weight.size(1) * prod(*kernel_size)
> For more details see: https://github.com/pytorch/pytorch/issues/15314#issuecomment-477448573"
The reason we permute the weights before init is because `kaiming_uniform_` uses the number of in and out
features for initialization, which are computed as tensor.size(0) and tensor.size(1). However, these
dimensions do not correspond for my weights.
"""
if self.groups == self.out_channels:
nn.init.kaiming_uniform_(self.weight.permute(3, 0, 1, 2), a=math.sqrt(5))
else:
nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
def forward(self, x: torch.Tensor):
if self.groups == 1:
return self.forward_matmul(x)
else:
return self.forward_elementwise(x)
def forward_matmul(self, x: torch.Tensor):
if self.padding == "same":
padded_x = self._pad_pre_conv(x)
else:
padded_x = x
bs, _, seq_len = padded_x.size()
padded_x = padded_x.transpose(-1, -2)
# Size: (bs, seq_len+pad, hidden)
out = padded_x.matmul(self.weight.view(self.weight.size(0), -1)).view(bs, seq_len, self.out_channels, -1)
# Size: (bs, seq_len+pad, hidden, kernel_size)
return out.permute(0, 2, 3, 1)
def forward_elementwise(self, x: torch.Tensor):
assert len(x.size()) == 3
assert x.size(1) == self.out_channels
# Size: (bs, hidden, seq_len)
if self.padding == "same":
padded_x = self._pad_pre_conv(x)
else:
padded_x = x
# Unsqueeze for broadcasting with the kernel_size dim of the filters
padded_x = padded_x.transpose(-1, -2).unsqueeze(2)
# Size: (bs, seq_len, 1, hidden)
out = padded_x * self.weight
# Size: (bs, seq_len, kernel_size, hidden)
return out.transpose(1, 3)
def _pad_pre_conv(self, inp: torch.Tensor):
"""
Pad with zeros at the beginning and end just like `nn.Conv1d`.
"""
return nn.functional.pad(inp, self.pad, "constant", 0.0)
def extra_repr(self):
return "in_features={}, out_features={}, kernel_size={}, groups={}".format(
self.in_channels, self.out_channels, self.kernel_size, self.groups
)
class MantaCachedConvolutionPooling(nn.Module):
def __init__(
self,
padding_length,
output_dim,
kernel_size,
hidden_dim,
depthwise_convolution,
variance_regularization,
mean_pool,
):
super().__init__()
self.padding_length = padding_length
self.output_dim = output_dim
self.kernel_size = kernel_size
self.hidden_dim = hidden_dim
self.depthwise_convolution = depthwise_convolution
self.variance_regularization = variance_regularization
self.mean_pool = mean_pool
if isinstance(self.kernel_size, int):
self.kernel_size = [[self.kernel_size, hidden_dim]]
self.conv_output_dim = sum([k_dim[1] for k_dim in self.kernel_size])
# Since the sum of the hidden dimensions of all the filters might not match the language model hidden size, we
# specify it here
self.out_projection = nn.Linear(self.conv_output_dim, self.output_dim, bias=True)
self.conv_layers = nn.Sequential(
*[
MantaConvFeatures(self.hidden_dim, h, k, groups=h if self.depthwise_convolution else 1, padding="same")
for (k, h) in self.kernel_size
]
)
self.eps = None
self.conv_layer = None
def forward(self, unconstrained_separation_probs: torch.Tensor, byte_embeddings: torch.Tensor):
device = unconstrained_separation_probs.device
if self.eps is None:
self.eps = 5 * torch.finfo(unconstrained_separation_probs.dtype).resolution
self.variance_regularization = max(self.eps, self.variance_regularization)
if self.conv_layer is not None:
self.conv_layer = self.conv_layer.to(device)
batch_size, seq_len = byte_embeddings.shape[:2]
# We set the probability of the first token to be 0 therwise the cumsum will not work
separation_probs = unconstrained_separation_probs.clone()
separation_probs[:, 0] = 0
assert separation_probs.shape == (batch_size, seq_len)
# Compute the moments of the block_id random variable
block_id_expectation = separation_probs.cumsum(axis=-1)
block_id_std = torch.sqrt(
(separation_probs * (1.0 - separation_probs)).cumsum(axis=-1) + self.variance_regularization
)
# Get the maximum number of blocks
max_nb_blocks = min(seq_len, (block_id_expectation + 3 * block_id_std).max().int().item() + 1)
possible_blocks_id = torch.arange(max_nb_blocks).to(device)
# Get the block/byte proba using the Gaussian PDF
log_scale = block_id_std[:, None, :].log()
log_proba = (
-((block_id_expectation[:, None, :] - possible_blocks_id[None, :, None]) ** 2)
/ (2 * block_id_std[:, None, :])
- log_scale
- math.log((2 * math.pi) ** 0.5)
)
block_byte_proba = log_proba.softmax(-2)
token_size = block_byte_proba.sum(-1, keepdim=True)
regularized_token_size = torch.maximum(token_size, torch.ones_like(token_size))
if self.mean_pool:
block_byte_proba_normalized = block_byte_proba / regularized_token_size
else:
# Makes no sense to regularize using sequence length in the max_pooling case.
block_byte_proba_normalized = block_byte_proba
block_embeddings = self.pooling(byte_embeddings, block_byte_proba_normalized)
pad_length = min(self.padding_length, max_nb_blocks)
block_embeddings = pad_block_embeddings(block_embeddings, pad_length)
block_embeddings = self.out_projection(block_embeddings)
return block_embeddings
def pooling(self, embeddings: torch.Tensor, block_byte_proba: torch.Tensor):
block_embeddings = []
for conv_layer in self.conv_layers:
# First, compute the convolution maps SEPARATELY, i.e. without summing them together, only the element wise multiplication
# This is similar to a cache that we'll reuse for each block probabilities.
features = conv_layer(embeddings.transpose(1, 2)).permute(0, 3, 1, 2)
# Size : (batch_size, seq_len + padding, hidden_dim, kernel_size)
pad = conv_layer.pad
for i in range(0, conv_layer.kernel_size):
# We shift like that to match the padding done inside `conv_layer`
features[..., i] = features[..., i].roll(pad[0] - i, 1)
# Cut out the padded vector to obtain the right sequence length at the end
features = features[:, pad[1] : features.size(1) - pad[0]]
# Size : (batch_size, seq_len, hidden_dim, kernel_size)
# Then, artificially sum the convolution features by shifting the input bytes
padded_block_byte_proba = nn.functional.pad(block_byte_proba, pad, "constant", 0.0)
expanded_block_byte_proba = []
for i in range(0, conv_layer.kernel_size):
rolled_proba = padded_block_byte_proba.clone().roll(pad[0] - i, -1)
expanded_block_byte_proba.append(rolled_proba)
expanded_block_byte_proba = torch.stack(expanded_block_byte_proba, -1)
# We use :tensor.size(2) - pad instead of just :-pad because if pad = 0, we have an undesired behaviour where the whole sequence is removed
expanded_block_byte_proba = expanded_block_byte_proba[
:, :, pad[1] : expanded_block_byte_proba.size(2) - pad[0], :
]
# Size : (batch_size, block_size, seq_len, kernel_size)
if self.mean_pool:
convolved = torch.einsum("b s h k, b B s k -> b B h", features, expanded_block_byte_proba)
else:
convolved = torch.einsum("b s h k, b B s k -> b B s h", features, expanded_block_byte_proba)
convolved = convolved.max(dim=-2).values
block_embeddings.append(convolved)
block_embeddings = torch.cat(block_embeddings, dim=-1)
return block_embeddings
class MantaPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = MantaConfig
base_model_prefix = "transformer"
supports_gradient_checkpointing = True
def _init_weights(self, module):
"""Initialize the weights"""
pass
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, (T5Attention, T5Stack)):
module.gradient_checkpointing = value
def _shift_right(self, input_ids):
decoder_start_token_id = self.config.decoder_start_token_id
pad_token_id = self.config.pad_token_id
assert decoder_start_token_id is not None, (
"self.model.config.decoder_start_token_id has to be defined. In T5 it is usually set to the pad_token_id."
" See T5 docs for more information"
)
# shift inputs to the right
if is_torch_fx_proxy(input_ids):
# Item assignment is not supported natively for proxies.
shifted_input_ids = torch.full(input_ids.shape[:-1] + (1,), decoder_start_token_id)
shifted_input_ids = torch.cat([shifted_input_ids, input_ids[..., :-1]], dim=-1)
else:
shifted_input_ids = input_ids.new_zeros(input_ids.shape)
shifted_input_ids[..., 1:] = input_ids[..., :-1].clone()
shifted_input_ids[..., 0] = decoder_start_token_id
assert pad_token_id is not None, "self.model.config.pad_token_id has to be defined."
# replace possible -100 values in labels by `pad_token_id`
shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
return shifted_input_ids
@add_start_docstrings(
"The bare Manta Model transformer outputting encoder's raw hidden-states without any specific head on top."
)
class MantaEncoderModel(MantaPreTrainedModel):
authorized_missing_keys = [
r"encoder.embed_tokens.weight",
]
def __init__(self, config: MantaConfig):
super().__init__(config)
self.byte_embeddings = nn.Embedding(config.vocab_size, config.byte_embedding_dim)
self.frontier_predictor = MantaFrontierPredictor(
hidden_size=config.byte_embedding_dim,
num_layers=config.frontier_predictor_num_layers,
num_attention_heads=config.frontier_predictor_num_attention_heads,
dropout_rate=config.dropout_rate,
attention_window=config.frontier_predictor_attention_window,
max_length=config.max_length_inputs,
)
self.pooler = MantaCachedConvolutionPooling(
padding_length=config.max_length_encoder_decoder,
output_dim=config.d_model,
kernel_size=config.pooling_kernel_size,
hidden_dim=config.byte_embedding_dim,
depthwise_convolution=config.pooling_depthwise_convolution,
variance_regularization=config.pooling_variance_regularization,
mean_pool=config.pooling_mean_pool,
)
self.t5_encoder = T5Stack(
T5Config(
d_model=config.d_model,
d_kv=config.d_kv,
d_ff=config.d_ff,
num_layers=config.num_layers,
num_heads=config.num_heads,
relative_attention_num_buckets=config.relative_attention_num_buckets,
relative_attention_max_distance=config.relative_attention_max_distance,
dropout_rate=config.dropout_rate,
layer_norm_epsilon=config.layer_norm_epsilon,
initializer_factor=config.initializer_factor,
feed_forward_proj=config.feed_forward_proj,
pad_token_id=config.pad_token_id,
eos_token_id=config.eos_token_id,
is_decoder=False,
use_cache=False,
)
)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.byte_embeddings
def set_input_embeddings(self, new_embeddings):
self.byte_embeddings = new_embeddings
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.t5_encoder.block[layer].layer[0].SelfAttention.prune_heads(heads)
def _compute_pooled_representations(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
):
if inputs_embeds is None and input_ids is None:
return None
byte_embeddings = inputs_embeds if inputs_embeds is not None else self.byte_embeddings(input_ids)
frontier_predictions = self.frontier_predictor(byte_embeddings, attention_mask)
pooled_representations = self.pooler(frontier_predictions, byte_embeddings)
return pooled_representations, frontier_predictions
@replace_return_docstrings(output_type=MantaBaseModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.FloatTensor], MantaBaseModelOutput]:
r"""
Returns:
Example:
```python
>>> from transformers import ByT5Tokenizer, MantaEncoderModel
>>> tokenizer = ByT5Tokenizer.from_pretrained("google/byt5-small")
>>> model = MantaEncoderModel.from_pretrained("nthngdy/manta-small")
>>> input_ids = tokenizer(
... "Studies have been shown that owning a dog is good for you", return_tensors="pt"
... ).input_ids # Batch size 1
>>> outputs = model(input_ids=input_ids)
>>> last_hidden_states = outputs.last_hidden_state
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
pooled_representations, frontier_predictions = self._compute_pooled_representations(
input_ids, attention_mask, inputs_embeds
)
encoder_outputs = self.t5_encoder(
inputs_embeds=pooled_representations,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if not return_dict:
return encoder_outputs + (frontier_predictions,)
return MantaBaseModelOutput(frontier_predictions=frontier_predictions, **encoder_outputs)
class MantaModel(MantaPreTrainedModel):
_keys_to_ignore_on_load_missing = [
r"encoder_decoder.encoder.embed_tokens.weight",
r"encoder_decoder.decoder.embed_tokens.weight",
]
_keys_to_ignore_on_load_unexpected = [
r"encoder_decoder.decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight",
]
def __init__(self, config: MantaConfig):
super().__init__(config)
self.encoder = MantaEncoderModel(config)
self.decoder_embeddings = nn.Embedding(config.vocab_size, config.d_model)
self.decoder = T5Stack(
T5Config(
vocab_size=config.vocab_size,
d_model=config.d_model,
d_kv=config.d_kv,
d_ff=config.d_ff,
num_layers=config.num_decoder_layers,
num_heads=config.num_heads,
relative_attention_num_buckets=config.relative_attention_num_buckets,
relative_attention_max_distance=config.relative_attention_max_distance,
dropout_rate=config.dropout_rate,
layer_norm_epsilon=config.layer_norm_epsilon,
initializer_factor=config.initializer_factor,
feed_forward_proj=config.feed_forward_proj,
use_cache=config.use_cache,
pad_token_id=config.pad_token_id,
eos_token_id=config.eos_token_id,
is_decoder=True,
is_encoder_decoder=False,
),
self.decoder_embeddings,
)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.encoder.get_input_embeddings()
def set_input_embeddings(self, new_embeddings):
self.encoder.set_input_embeddings(new_embeddings)
def get_encoder(self):
return self.encoder
def get_decoder(self):
return self.decoder
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
@replace_return_docstrings(output_type=MantaSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.BoolTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
decoder_head_mask: Optional[torch.FloatTensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
inputs_embeds: Optional[torch.Tensor] = None,
decoder_inputs_embeds: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.FloatTensor], MantaSeq2SeqLMOutput]:
r"""
Returns:
Example:
```python
>>> from transformers import ByT5Tokenizer, MantaModel
>>> tokenizer = ByT5Tokenizer.from_pretrained("google/byt5-small")
>>> model = MantaModel.from_pretrained("nthngdy/manta-small")
>>> input_ids = tokenizer(
... "Studies have been shown that owning a dog is good for you", return_tensors="pt"
... ).input_ids # Batch size 1
>>> decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids # Batch size 1
>>> # preprocess: Prepend decoder_input_ids with start token which is pad token for MantaModel.
>>> # This is not needed for torch's MantaForConditionalGeneration as it does this internally using labels arg.
>>> decoder_input_ids = model._shift_right(decoder_input_ids)
>>> # forward pass
>>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
>>> last_hidden_states = outputs.last_hidden_state
```"""
use_cache = use_cache if use_cache is not None else self.config.use_cache
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if encoder_outputs is None:
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
elif return_dict and not isinstance(encoder_outputs, MantaBaseModelOutput):
encoder_outputs = MantaBaseModelOutput(
last_hidden_state=encoder_outputs[0],
hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
frontier_predictions=encoder_outputs[3] if len(encoder_outputs) > 3 else None,
)
hidden_states = encoder_outputs[0]
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
encoder_hidden_states=hidden_states,
encoder_attention_mask=attention_mask,
inputs_embeds=decoder_inputs_embeds,
head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if not return_dict:
return decoder_outputs + encoder_outputs
return MantaSeq2SeqLMOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
past_key_values=decoder_outputs.past_key_values,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
encoder_hidden_states=encoder_outputs.hidden_states,
encoder_attentions=encoder_outputs.attentions,
frontier_predictions=encoder_outputs.frontier_predictions,
)
@add_start_docstrings("""Manta Model with a `language modeling` head on top.""")
class MantaForConditionalGeneration(MantaPreTrainedModel):
_keys_to_ignore_on_load_missing = [
r"encoder.embed_tokens.weight",
r"decoder.embed_tokens.weight",
r"lm_head.weight",
]
_keys_to_ignore_on_load_unexpected = [
r"decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight",
]
def __init__(self, config: MantaConfig):
super().__init__(config)
self.model_dim = config.d_model
self.encoder = MantaEncoderModel(config)
self.decoder_embeddings = nn.Embedding(config.vocab_size, config.d_model)
self.decoder = T5Stack(
T5Config(
vocab_size=config.vocab_size,
d_model=config.d_model,
d_kv=config.d_kv,
d_ff=config.d_ff,
num_layers=config.num_decoder_layers,
num_heads=config.num_heads,
relative_attention_num_buckets=config.relative_attention_num_buckets,
relative_attention_max_distance=config.relative_attention_max_distance,
dropout_rate=config.dropout_rate,
layer_norm_epsilon=config.layer_norm_epsilon,
initializer_factor=config.initializer_factor,
feed_forward_proj=config.feed_forward_proj,
use_cache=config.use_cache,
pad_token_id=config.pad_token_id,
eos_token_id=config.eos_token_id,
is_decoder=True,
is_encoder_decoder=False,
),
self.decoder_embeddings,
)
self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.encoder.get_input_embeddings()
def set_input_embeddings(self, new_embeddings):
self.encoder.set_input_embeddings(new_embeddings)
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def get_output_embeddings(self):
return self.lm_head
def get_encoder(self):
return self.encoder
def get_decoder(self):
return self.decoder
@replace_return_docstrings(output_type=MantaSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.BoolTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
decoder_head_mask: Optional[torch.FloatTensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[Tuple[Tuple[torch.Tensor]]] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.FloatTensor], MantaSeq2SeqLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[-100, 0, ...,
config.vocab_size - 1]`. All labels set to `-100` are ignored (masked), the loss is only computed for
labels in `[0, ..., config.vocab_size]`
Returns:
Examples:
```python
>>> from transformers import ByT5Tokenizer, MantaForConditionalGeneration
>>> tokenizer = ByT5Tokenizer.from_pretrained("google/byt5-small")
>>> model = MantaForConditionalGeneration.from_pretrained("nthngdy/manta-small")
>>> # training
>>> input_ids = tokenizer("The <extra_id_0> walks in <extra_id_1> park", return_tensors="pt").input_ids
>>> labels = tokenizer("<extra_id_0> cute dog <extra_id_1> the <extra_id_2>", return_tensors="pt").input_ids
>>> outputs = model(input_ids=input_ids, labels=labels)
>>> loss = outputs.loss
>>> logits = outputs.logits
>>> # inference
>>> input_ids = tokenizer(
... "summarize: studies have shown that owning a dog is good for you", return_tensors="pt"
... ).input_ids # Batch size 1
>>> outputs = model.generate(input_ids)
>>> print(tokenizer.decode(outputs[0], skip_special_tokens=True))
>>> # studies have shown that owning a dog is good for you.
```"""
use_cache = use_cache if use_cache is not None else self.config.use_cache
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
if head_mask is not None and decoder_head_mask is None:
if self.config.num_layers == self.config.num_decoder_layers:
warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning)
decoder_head_mask = head_mask
# Encode if needed (training, first prediction pass)
if encoder_outputs is None:
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
elif return_dict and not isinstance(encoder_outputs, MantaBaseModelOutput):
encoder_outputs = BaseModelOutput(
last_hidden_state=encoder_outputs[0],
hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
frontier_predictions=encoder_outputs[3] if len(encoder_outputs) > 3 else None,
)
hidden_states = encoder_outputs[0]
if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None:
# get decoder inputs from shifting lm labels to the right
decoder_input_ids = self._shift_right(labels)
# Decode
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
inputs_embeds=decoder_inputs_embeds,
past_key_values=past_key_values,
encoder_hidden_states=hidden_states,
head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = decoder_outputs[0]
if self.config.tie_word_embeddings:
# Rescale output before projecting on vocab
# See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
sequence_output = sequence_output * (self.model_dim**-0.5)
lm_logits = self.lm_head(sequence_output)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss(ignore_index=-100)
loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))
# TODO(thom): Add z_loss https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L666
if not return_dict:
output = (lm_logits,) + decoder_outputs[1:] + encoder_outputs
return ((loss,) + output) if loss is not None else output
return MantaSeq2SeqLMOutput(
loss=loss,
logits=lm_logits,
past_key_values=decoder_outputs.past_key_values,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
encoder_hidden_states=encoder_outputs.hidden_states,
encoder_attentions=encoder_outputs.attentions,
frontier_predictions=encoder_outputs.frontier_predictions,
)
def prepare_inputs_for_generation(
self,
input_ids,
past=None,
attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
use_cache=None,
encoder_outputs=None,
**kwargs
):
# cut decoder_input_ids if past is used
if past is not None:
input_ids = input_ids[:, -1:]
return {
"decoder_input_ids": input_ids,
"past_key_values": past,
"encoder_outputs": encoder_outputs,
"attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
"use_cache": use_cache,
}
def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
return self._shift_right(labels)
def _reorder_cache(self, past, beam_idx):
# if decoder past is not included in output
# speedy decoding is disabled and no need to reorder
if past is None:
logger.warning("You might want to consider setting `use_cache=True` to speed up decoding")
return past
reordered_decoder_past = ()
for layer_past_states in past:
# get the correct batch idx from layer past batch dim
# batch dim of `past` is at 2nd position
reordered_layer_past_states = ()
for layer_past_state in layer_past_states:
# need to set correct `past` for each of the four key / value states
reordered_layer_past_states = reordered_layer_past_states + (
layer_past_state.index_select(0, beam_idx.to(layer_past_state.device)),
)
assert reordered_layer_past_states[0].shape == layer_past_states[0].shape
assert len(reordered_layer_past_states) == len(layer_past_states)
reordered_decoder_past = reordered_decoder_past + (reordered_layer_past_states,)
return reordered_decoder_past